Artificial Intelligence-Driven (AI-Driven) digital technologies (DT) are intrinsically connected to interact, perceive, and understand people, businesses, economies, and lives in general. The term Artificial Intelligence (AI) can be understood as a general combination and integration of applications with other “DTs” to create machines capable of thinking like humans. AI-Driven DT economic and societal impacts increase on a continuous basis and more recently they are assuming an important role in the Sustainable Development Goals (SDG) Agenda 2030, and their implementations are a considerable decision for developed and developing countries. In turn, Brazil and Portugal have been elected in this research to display their view on AI-driven DT on SDG achievements, contradicting their perspectives in this field.
1. Introduction
Artificial Intelligence-Driven (AI-Driven) digital technologies (DT) are intrinsically connected to interact, perceive, and understand people, businesses, economies, and lives in general
[1]. The term Artificial Intelligence (AI) can be understood as a general combination and integration of applications with other “DTs” to create machines capable of thinking like humans
[1][2][3]. AI-Driven DT economic and societal impacts increase on a continuous basis and more recently they are assuming an important role in the Sustainable Development Goals (SDG) Agenda 2030
[4], and their implementations are a considerable decision for developed and developing countries. In turn, Brazil and Portugal have been elected in this research to display their view on AI-driven DT on SDG achievements, contradicting their perspectives in this field.
As we enter into the age of sustainable development
[5], in which the 17 SDGs (discussed in
Section 2) are guiding nations of the world, Artificial Intelligence (AI) and digital technologies (DT)—such as digital twins, blockchain, virtual and augmented reality, and big data, among others—create a high expectation in enhancing economic, environment, and societal levels in global transformation to attend to SDGs
[1]. Some priorities or key elements are pointed out by Palomares et al. (2021) to have optimal results and impacts on AI usage on the SDGs, such as (i) AI-driven DT fueled by universally accessible and reliable data; (ii) strengthen science–industry–government dialogue for technology and knowledge transferring; (iii) adapt and coordinate action plans in each country and context; (iv) alternative standards for facilitating the evaluation of SDG attainment; and (v) lessons learnt since the pandemic
[1].
Numerous studies also identified advantages in economic and environmental evaluation by an AI-Driven DT application, leveraging cleaner production—mainly in industry segments
[6][7][8][9]—for more productive processes; less toxic and more biodegradable products; and increased efficiency of raw materials, water, and energy, aiming at non-generation, reduction, or recycling of waste
[7]. According to Oliveira Neto et al.
[7], the environmental advantages from the adoption of cleaner production are concentrated and indirectly related to Industry, Innovation, and Infrastructure (SDG 9); Responsible Production and Consumption (SDG 12); and protection of Life on Land (SDG 15).
Due to a roller coaster of AI-driven DT success and failure in a consistent pattern over AI history
[2][10][11], many countries pursue different strategies to reach SDG incorporating technologies in various forms through experimentation, public policies, triple helix, joint-ventures, global alliances, or SDG technological programs. Specialized professionals are involved in many of these initiatives to figure out solutions to apply AI-driven DT with a lower-cost impact to overcome country boundaries. In this vein, this study aims to compare the preference for artificial intelligence-driven digital technologies (AI-Driven DT) to achieve SDGs in Brazil and Portugal.
This study points out a ranking of preferable AI-Driven DT for the different SDGs in these two countries—Brazil and Portugal—conducting a public survey describing and classifying a set of technologies recommended for each SDG. The respondents are experts, executives involved in sustainability, and interested people who classified some AI-driven DT considered the most appropriate to reach SDG goals.
A final analysis is performed for each SDG along with preferred AI-Driven DT comparing similarities and discrepancies between these countries, with special attention on SDG 9, 12, and 15, which are related to cleaner production. Brazil and Portugal have different preferences in terms of SDG priorities and AI-Driven DT, with the former prioritizing DT that supports education aspects and the latter prioritizing those that support small and medium companies.
2. AI-Driven DTs and Sustainable Development Goals
In this section, we introduce a brief description of AI-Driven DT into the 17 SDGs proposed by the United Nations Agenda 2030
[4] under three dimension perspectives
[1][3] and their contributions and barriers to attain the different aspects of each SDG. In a sequence, we present a panorama of Brazil and Portugal and their efforts to achieve SDGs.
2.1. Contributions and Barriers of AI-Driven DT on SDG
The AI definition
[12], from a pragmatic stand point, was presented by some authors as a computational natural language process capable of communicating with human beings through visual information, reasonable knowledge representation for decision-making, mind-thinking automated to process knowledge stored, and machine learning to extract common patterns from the available information. However, AI itself is not enough because it depends on data flow to learn and demands from other DTs for proper performance.
Therefore, AI-driven DT also faces challenges related to fairness, accountability, transparency, and ethics (FATE) for a reliable development, and the aim is to provide robustness to systems to avoid damages, lawfulness required by regulations and laws, and ethicalness to respect freedom, dignity, equality, citizen-rights, or non-discrimination principles
[3][13].
Many of these are AI systems connected with DTs. Then, we briefly point out some of those that are used as DTs in the questionnaire applied in this study: Internet of Things (IoT), Blockchain, Augmented Reality, Virtual Reality, Digital Twin, 5G Communication Infrastructure, Big Data, and Recommender and Information Systems.
The wealthiness of alternatives to solving problems through AI-Driven DTs may support countries in reaching SDGs rapidly; however, they may result in inequalities due to the restriction of educational and computing resources throughout the world—mainly in developing countries, among other challenges. Additionally, a wide range of DTs are being developed that affect individuals lives, as well as impact economic, environmental, and societal aspects, requiring piloting new approaches and procedures from governments on the purpose of achieving SDGs
[14].
Contributions and barriers for AI-driven DT are evidenced by some authors in the literature
[1][2][3][14]; thus, we present in
Table 1 a summary of the AI-Driven DT analysis based on the categorization among the SDGs, presented by Palomares et al.
[1], relating to economy, environmental, and societal aspects. We highlight the economic dimension as sustainability and individual welfare concentration at an economic level, considering the welfare itself and prosperity; at the environmental dimension, safeguarding and preserving the environment, as well as the sustainable management of resources; at the social dimension, a sustainable development regarding welfare, prosperity at the community level, and equality are considered
[14][15].
Table 1. Brief contributions and barriers of AI-Driven DT usage on SDGs.
2.2. A Panoram of SDG in Brazil and Portugal
In this subsection, we present the main strategic initiatives from Brazil and Portugal to disseminate SDGs and move forward to new achievements in the SDG Agenda 2030.
2.2.1. Brazil Initiatives for SDG
In general, countries are failing to adopt SDGs around the globe
[36]. Brazil has been progressing in the SDG Agenda 2030, particularly in the field of decent work and eco-nomic growth. Nonetheless, considering Brazil’s reality, sustainable development is still far away from Brazilian households. Poor existent sanitary infrastructure to handle plastics residuals, solid trash, sewage, and particularly kitchen oil which are deposited in many regions protected by national laws are some challenges faced.
[37].
Ali et al.
[36] pointed out that Brazilian companies are more focused on economic activities, development of institutions, and securing respectable work opportunities for their people. However, three SDG goals are not highlighted in the vision and mission statements of the Brazilian companies: ‘Quality Education’, ‘Gender Equality’, and ‘Life Below Water’.
In the scenario of diversity that defines Brazil, some strategies were defined as essential for SDG achievements: (i) national governance through the creation of the National Commission for the SDGs as an advisory and parity body; (ii) adequacy of targets to take into account regional diversity, Brazilian government priorities, national development plans, current legislation, and the socioeconomic situation experienced by the country; and (iii) national indicators considering data availability and monitoring
[37].
In addition to the Brazilian government’s planning instruments
[37], Brazil intends to stimulate the creation of local governance structures, which will lead the process of localizing the 2030 Agenda in the territories encompassing an engagement of private sector, academia and civil society organizations, preparation of monitoring reports, building institutional partnerships, preparation of a multi-year plan, creation of subnational commissions, SDG Brazil Award, and training public managers. Some tools by the initiative of the government and civil society have supported the planning and the dissemination of the SDGs, which are highlighted in
Table 2.
Table 2. Brazilian initiatives for SDG dissemination.
2.2.2. Portugal Initiatives for SDG
Portugal has followed up its SDG statistics through the Instituto Nacional de Estatística (INE) as the central institution for the production and dissemination of official statistics of Portugal. They have been in close coordination with other statistical departments of various ministries and national authorities, involved in the implementation of SDG strategic priorities to gather efforts in achieving the Agenda 2030
[38].
The country has been strongly involved in the efforts undertaken by other international bodies to align its respective policies and instruments to the SDG ambitions. In 2016 at the Conference of the Parties to the United Nations Framework Convention on Climate Change, Portugal took on the goal of achieving carbon neutrality by 2050, having developed a roadmap for carbon neutrality that set the vision, the trajectories, and guidelines for the policies to be implemented in this time frame.
The response to this challenge will be truly transformational in the way in which some of the most determining aspects of life in society face, regarding production and consumption patterns; the relationship with this production and use of energy; the way in which cities and spaces are organized for housing, work, and leisure; and the way mobility needs are faced with. In addition to being a technological challenge, this will also be a societal challenge that will depend considerably on the support and adhesion of the entire society.
Portugal embodies its strategic priorities
[38] for the implementation of the 2030 Agenda for Sustainable Development in SDG4—Quality Education, SDG5—Gender Equality, SDG9—Industry, Innovation, and Infrastructure, SDG10—Reducing Inequalities, SDG13—Climate Action, and SDG14—Protecting Marine Life. At the same time, the INE has been monitoring European initiatives in a framework of cooperation with the United Nations Economic Commission for Europe (UNECE) and Eurostat in developing global indicators. In this context, we note a differentiated situation in terms of methodological stabilization and availability of these indicators, according to the classification system defined by the Inter-agency Expert Group (IAEG-SDG)
[38].
This process has enabled national and international mapping of available information and identified the most appropriate sources of indicators for the monitoring of the 17 SDGs in Portugal.
Figure 1 shows information and data availability in terms of indicators to support SDG achievements in Portugal, published in a national report on the implementation of the 2030 Agenda (in the graphic are % of indicators)
[38].
Figure 1. Quantity of information and data availability for each SDG (in percentage). Source: Ministry of Foreign Affairs National Report on the Implementation of the 2030 Agenda for Sustainable Development
[38].
In terms of indicators to be developed, the Instituto Nacional de Estatísticas—INE— highlights more attention to SDG 2, 5, 11, and 14, where there are a higher percentage of inconclusive data availability, together with SDG 10, 11, 12, 13, 14, 15, and 17 that have indicators out of scope to measure the evolution of these SDGs. These SDGs reflect a data disaggregation to map the progress of SDGs in the various population sectors, which have received more attention from Portugal’s authorities.
To focus all the existing information on a single platform, the INE has made available on its portal (
www.ine.pt; accessed on 10 October 2021) a file on the “Sustainable Development Goals” (in continuous updating) to allow all interested users an easy overview of SDG indicators. In the context of international cooperation, the INE has also been supporting the Portuguese-speaking countries in developing their national statistical systems in the context of the Community of Portuguese Language Countries (CPLP). In this sense, the statistical cooperation has been, since the 1980s, one of the priorities of INE and of the Portuguese Cooperation—today, meeting objectives in SDG 17. The existing cooperation programs will reflect the new information needs, with particular emphasis on data disaggregation to reflect progress in the various sectors of the population, including the most vulnerable
[38].